Building a Machine-Learning Framework to Remotely Assess Parkinson's Disease Using Smartphones

• Published in: IEEE transactions on biomedical engineering Vol. 67; no. 12; pp. 3491 - 3500
• Main Authors: Chen, Oliver Y., Lipsmeier, Florian, Phan, Huy, Prince, John, Taylor, Kirsten I., Gossens, Christian, Lindemann, Michael, Vos, Maarten de
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: <inline-formula xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <tex-math notation="LaTeX"> P \gg N</tex-math> </inline-formula> problem; Balance; Data analysis; Feature extraction; feature-selection; Gait; Learning algorithms; Machine learning; Movement disorders; Muscle contraction; Muscles; Muscular function; Neurodegenerative diseases; Parkinson's disease; Physicians; predictive modeling; Regularization; remote disease assessment; Remote monitoring; Remote sensors; Signs and symptoms; Smart phones; Smartphones; Smooth muscle; Telemedicine; Tremor
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2020.2988942

Objective: Parkinson's disease (PD) is a neurodegenerative disorder that affects multiple neurological systems. Traditional PD assessment is conducted by a physician during infrequent clinic visits. Using smartphones, remote patient monitoring has the potential to obtain objective behavioral data semi-continuously, track disease fluctuations, and avoid rater dependency. Methods: Smartphones collect sensor data during various active tests and passive monitoring, including balance (postural instability), dexterity (skill in performing tasks using hands), gait (the pattern of walking), tremor (involuntary muscle contraction and relaxation), and voice. Some of the features extracted from smartphone data are potentially associated with specific PD symptoms identified by physicians. To leverage large-scale cross-modality smartphone features, we propose a machine-learning framework for performing automated disease assessment. The framework consists of a two-step feature selection procedure and a generic model based on the elastic-net regularization. Results: Using this framework, we map the PD-specific architecture of behaviors using data obtained from both PD participants and healthy controls (HCs). Utilizing these atlases of features, the framework shows promises to (a) discriminate PD participants from HCs, and (b) estimate the disease severity of individuals with PD. Significance: Data analysis results from 437 behavioral features obtained from 72 subjects (37 PD and 35 HC) sampled from 17 separate days during a period of up to six months suggest that this framework is potentially useful for the analysis of remotely collected smartphone sensor data in individuals with PD.